Method for treating pulmonary exacerbation and disease progression in subjects having cystic fibrosis

ABSTRACT

The present invention is related to a method for determining pulmonary disease progression severity in a subject having cystic fibrosis and treating the subject according to the severity. The method comprises obtaining a whole blood sample from the subject; detecting the mRNA expression level of each of the following genes: TLR2, ADAM9, PLXND1, CD163, CD36, CD64, CSPG2, IL32, HPSE, HCA112; determining the severity of the pulmonary disease progression based on the subject&#39;s combined mRNA expression level of the genes; and treating the subject.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 119(e)to U.S. Provisional Patent Application Ser. No. 61/968,960, filed Mar.21, 2014 and to U.S. Provisional Patent Application Ser. No. 62/000,903,filed May 20, 2014, which are incorporated herein by reference.

FIELD OF THE INVENTION

The field of the present invention involves the categorization andtreatment of a population of subjects that are at risk for increasedpulmonary exacerbation and disease progression including an increase inmorbidity and/or mortality associated with severe pulmonaryexacerbation.

BACKGROUND

Cystic fibrosis (CF) impacts 30,000 individuals in the United States and70,000 individuals worldwide (Patient registry: Annual Data Report.Cystic Fibrosis Foundation 2012; Bethesda, Md.). Mortality from thedisease primarily occurs due to progressive respiratory infection and anexcessive inflammatory response in the CF lung (Davis P B, Drumm M,Konstan M W. Cystic fibrosis. Am J Respir Crit Care Med 1996;154:1229-56; Chmiel J F, Berger M, Konstan M W. The role of inflammationin the pathophysiology of CF lung disease. Clin Rev Allergy Immunol2002; 23:5-27). As the disease progresses, patients experienceincreasingly frequent pulmonary exacerbations, which in turn increaserisk for subsequent decline (Sanders D B, Hoffman L R, Emerson J, et al.Return of FEV1 after pulmonary exacerbation in children with cysticfibrosis. Pediatr Pulmonol 2010; 45:127-34; Sanders D B, Bittner R C,Rosenfeld M, Hoffman L R, Redding G J, Goss C H. Failure to recover tobaseline pulmonary function after cystic fibrosis pulmonaryexacerbation. Am J Respir Crit Care Med 2010; 182:627-3; Waters V,Stanojevic S, Atenafu E G, et al. Effect of pulmonary exacerbations onlong-term lung function decline in cystic fibrosis. Eur Respir J 2012;40:61-6). The number of pulmonary exacerbation episodes suffered in asingle year correlates highly with lung function decline in the ensuingthree years for both children and adults (Sanders D B, Hoffman L R,Emerson J, et al. Return of FEV1 after pulmonary exacerbation inchildren with cystic fibrosis. Pediatr Pulmonol 2010; 45:127-34; SandersD B, Bittner R C, Rosenfeld M, Hoffman L R, Redding G J, Goss C H.Failure to recover to baseline pulmonary function after cystic fibrosispulmonary exacerbation. Am J Respir Crit Care Med 2010; 182:627-32;Sanders D B, Bittner R C, Rosenfeld M, Redding G J, Goss C H. Pulmonaryexacerbations are associated with subsequent FEV1 decline in both adultsand children with cystic fibrosis. Pediatr Pulmonol 2011; 46:393-400).An exceedingly high number of CF patients, 1 in 4, do not recover tobaseline Forced Expiratory Volume in 1 second (FEV₁) after standardtreatment of acute pulmonary exacerbations (APE) (Sanders D B, Hoffman LR, Emerson J, et al. Return of FEV1 after pulmonary exacerbation inchildren with cystic fibrosis. Pediatr Pulmonol 2010; 45:127-34).

Cystic fibrosis (CF) is the most common lethal inherited disease in thewestern world. While life expectancies have increased to nearly 40years, respiratory failure still accounts for >80% of deaths from thedisease, usually in young adults in the third or fourth decade of life.The triad of airway obstruction with mucus, chronic endobronchialinfection with pathogens such as Pseudomonas aeruginosa, and severeairway inflammation, are the major pathogenic factors in CF lung disease(Konstan, 1998, Clin Chest Med 19(3):505-13, vi). Given the shortage ofsolid organs for transplantation in end stage lung disease, there is acritical need for effective anti-microbial and anti-inflammatorytherapies to mitigate progression of disease in this young population.

The rendering of rapid and efficient clinical trials in CF and otherdiseases associated with pulmonary exacerbation, is hampered, in part,by the lack of sensitive measures of treatment response. In general,severity of exacerbation episodes are estimated based on spirometry andsymptoms (Shoki A H, Mayer-Hamblett N, Wilcox P G, Sin D D, Quon B S.Systematic review of blood biomarkers in cystic fibrosis pulmonaryexacerbations. Chest 2013; 144:1659-70). No mechanism exists to quantifyor to “stage” the degree of inflammation in a particular individual, ina manner which predicts their risk from the infectious episode. Such atool could potentially capture the marked heterogeneity in exacerbationsbetween individuals, as well as within an individual, from one episodeto another. Identification of molecular phenotypes underlyingexacerbation heterogeneity would improve understanding of the individualhost's response to pulmonary infection, ideally allowing customizationof treatment approaches during the episode and beyond it. In currentpractice, consensus guidelines for APE treatment are relatively limited,in part due to a lack of definitions and classifiers of APE severity.There are no class A recommendations, and recommendations regarding keyaspects of treatment are deemed “indeterminate” for the following: drugselection, quantity of antimicrobial agents, dosing strategies, andduration of antibiotics (Flume P A, Mogayzel P J, Jr., Robinson K A, etal. Cystic fibrosis pulmonary guidelines: treatment of pulmonaryexacerbations. Am J Respir Crit Care Med 2009; 180:802-8). The abilityto readily identify and quantify host inflammatory responses may allowstratification of treatment according to underlying biology,facilitating the conduct of clinical trials to identify strategies toimprove current APE outcomes.

At present, there are no known reliable and sensitive molecular markerswhich can be used to categorize subject subgroups at risk for increasedpulmonary exacerbation and/or disease progression, including increasedrisk of mortality and/or morbidity due to severe acute pulmonaryexacerbation. There is a critical need in CF to better understand theimpact a particular exacerbation has on a patient's overall diseasecourse.

SUMMARY OF INVENTION

One embodiment of the invention relates to a method for categorizing asubject at risk for increased pulmonary exacerbation disease progressionwherein the subject has experienced a pulmonary exacerbation, the methodcomprising (a) obtaining a biological sample from the subject; (b)detecting the expression level of one or more genes selected from thegroup consisting of TLR2, ADAM2, PLXND1, CD163, CD36, CD64, CSPG2, IL32,HPSE, HCA112 and combinations thereof; (c) comparing the expressionlevel of the one or more genes from the subject with gene expressionprofiles of the same genes known to correlate with increased pulmonaryexacerbation disease progression; and (d) categorizing the subject as atincreased risk for increased pulmonary exacerbation disease progressionwhen the subject's gene expression profile correlates to the geneexpression profile for increased pulmonary exacerbation diseaseprogression.

In one aspect of this embodiment, a subject identified as having anincreased risk for pulmonary exacerbation disease progression will havean increased risk of morbidity.

In one aspect of this embodiment, a subject identified as having anincreased risk for pulmonary exacerbation disease progression will havean increased risk of mortality.

In one aspect of this embodiment, a subject identified as having anincreased risk for pulmonary exacerbation disease progression will havean increased risk for exacerbation recurrence.

In one aspect of this embodiment, a subject identified as having anincreased risk for pulmonary exacerbation disease progression will havea shorter interval of exacerbation free time.

In one aspect of this embodiment, the method further comprises measuringthe subject's forced expiratory volume (FEV₁) and/or C-reactive protein(CRP) levels.

Another embodiment of the invention relates to a method to treat asubject at risk for increased pulmonary exacerbation wherein the subjecthas experienced a pulmonary exacerbation, the method comprising: (a)obtaining a biological sample from the subject; (b) detecting theexpression level of one or more genes selected from the group consistingof TLR2, ADAM9, PLXND1, CD163, CD36, CD64, CSPG2, IL32, HPSE, HCA112 andcombinations thereof; (c) comparing the expression level of the one ormore genes from the subject with gene expression profiles of the samegenes known to correlate with increased pulmonary exacerbation, whereincertain expression profiles identify the subject as at increased riskfor increased pulmonary exacerbation; and (d) treating the subjectaggressively if the subject is at risk for increased pulmonaryexacerbation.

In one aspect of this embodiment, the subject identified as having anincreased risk for pulmonary exacerbation will have an increased risk ofmorbidity, mortality, and/or an increased risk for exacerbationrecurrence.

In one aspect of any of the embodiments, the subject has been diagnosedas having a disease selected from the group consisting of cysticfibrosis, asthma, chronic pulmonary obstructive disease, emphysema,interstitial lung disease, bronchitis, acute respiratory distresssyndrome, and pneumonia. In one aspect, the subject has been diagnosedas having cystic fibrosis.

In one aspect of any of the embodiments, the biological sample is wholeblood, Peripheral Blood Mononuclear Cells (PBMCs), leuokocytes,monocytes, lymphocytes, basophils, or eosinophils. In one aspect, thebiological sample is whole blood.

In one aspect of any of the embodiments, the expression level of the oneor more genes is detected by quantitative PCR or flow cytometery.

In yet another aspect of the embodiments, the expression level of eachof the following genes TLR2, ADAM9, PLXND1, CD163, CD36, CD64, CSPG2,IL32, HPSE, and HCA112 is detected and compared.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows hierarchical cluster analysis of CF peripheral bloodsamples at the onset of acute pulmonary exacerbation (AEP). Clusteranalysis from leukocytes categorizes three distinct molecular clustersof gene expression, based on a 10-gene panel (cluster of differentiation64 (CD64), a disintegrin and metalloproteinase domain 9 (ADAMS), clusterof differentiation 36 (CD36), interleukin 32 (IL32), heparanase (HPSE),plexin D1 (PLXND1), hepatocellular carcinoma associated antigen 112(HCA112), versican (CSPG2), toll-like receptor 2 (TLR2), and cluster ofdifferentiation 163 (CD163)), with corresponding sample sizes of n=10(cluster 1), n=37 (cluster 2), and n=10 (cluster 3).

FIGS. 2A to 2J show the comparison of mean expression values of a10-gene panel between clusters. Error bars represent SEM. *p<0.05 basedon ANOVA analysis of log-transformed values with Tukey's MultipleComparison Test for differences between clusters.

FIGS. 3A to 3C show the clinical variable differences between clusters,specifically white blood cell (WBC) count, FEV₁% predicted and CRP. MeanWBC counts (10⁹/L), FEV₁% predicted, and C-reactive protein (CRP)(mg/dL) are demonstrated in column graphs, and error bars represent SEM.Differences between clusters were evaluated with ANOVA, followinglog-transformation of WBC counts and CRP.

FIG. 4 shows the association between gene expression at APE onset andtime until next exacerbation. Kaplan-Meier curves demonstratedifferences in time to next exacerbation (in days) between clusters.After adjusting for baseline FEV₁, age, and neutrophil cell counts (thelast of which remained in the model after backward selection), thedifference between clusters in time to exacerbation event remainedsignificant (Cluster 3 versus 1, hazard ratio or HR=3.9, p=0.03; andCluster 3 versus 2, HR=3.9, p=0.0002). In models with the clustervariable alone, Cluster 3 versus 1 had an HR of 6.6, p=0.0002, andCluster 3 versus 2 had a HR of 4.7, p=0.0001. The ability of FEV₁ aloneto predict time to next exacerbation had borderline significance(p=0.05). Adjusted P-values are expressed, for respective clusters,versus cluster 3. Overall p<0.0001.

FIG. 5 shows the association between gene expression at APE onset and acomposite morbidity and mortality outcome (death, transplant ortransplant referral, intensive care unit (ICU) admission for respiratorydecline). Kaplan-Meier curves demonstrate differences in adverse diseaseoutcomes between clusters. Adjusted p values are expressed, forrespective clusters, versus cluster 3. Overall p=0.0002.

FIG. 6 shows a heat map representing differential expression of genesbetween clusters. Expression of single genes (rows) for each subjectwithin the 3 clusters (columns) are depicted with a grey-scalecolor-coded histogram representing over- and under-expression for eachgene. Shades on the left side of the color key signifies reduced geneexpression, while shades on the right side of the color key indicateincreased expression.

FIGS. 7A and 7B show the association of gene expression with serumcytokine concentration, interleukin 6 (IL-6) and interferon gamma(IFNγ). Spearman correlations with a Fisher's Z transformation arepresented, with 95% confidence interval (CI).

DETAILED DESCRIPTION

The present invention generally relates to novel methods forcategorizing and/or treating subgroups of subjects having an increasedrisk for increased pulmonary exacerbation and/or disease progressionleading to pulmonary decline and in the treatment of a disease that isassociated with severe pulmonary exacerbation, such as CF. The inventionincludes the use of gene biomarkers whose expression patterns correlatewith severity degrees of pulmonary exacerbation and pulmonaryexacerbation disease progression, including an increased risk ofmorbidity and/or mortality. The methods of the present invention providegreater sensitivity, specificity and discriminatory capacity than theexisting methods that are based on measurements of FEV₁ alone and/or CRP(C-reactive protein) alone or in combination and when used inconjunction with measurements of FEV₁ and/or CRP to enhance thepredictive power of FEV₁ and CRP. Spirometry is a common pulmonaryfunction test for measuring lung function. Specifically, FEV₁ is theestablished standard for assessing pulmonary treatment response. WhenFEV₁ measurements are decreased, treatment is initiated. Following twoto three weeks of intravenous antibiotic therapy, FEV₁ measurements aretypically repeated as a quantitative measure of clinical response.Similarly, FEV₁ measurements are utilized as the gold standardmeasurement for treatment response in clinical trials.

The inventors are the first to identify biological pathways underpinningheterogeneity in CF clinical outcomes, in the context of pulmonaryexacerbations. Molecular quantification of inflammation has precedencein asthma and chronic obstructive pulmonary disease (COPD) whereactivation of particular inflammatory pathways corresponds to clinicalphenotypes, which differ in terms of underlying inflammation, outcomes,disease progression and response to treatment (Singh D, Fox S M,Tal-Singer R, Bates S, Riley J H, Celli B. Altered gene expression inblood and sputum in COPD frequent exacerbators in the ECLIPSE cohort.PLoS One 2014; 9:e107381; McGrath K W, Icitovic N, Boushey H A, et al. Alarge subgroup of mild-to-moderate asthma is persistentlynoneosinophilic. Am J Respir Crit Care Med 2012; 185:612-9; Carolan B J,Sutherland E R. Clinical phenotypes of chronic obstructive pulmonarydisease and asthma: recent advances. J Allergy Clin Immunol 2013;131:627-34; quiz 35). The incorporation of peripheral blood genesignatures adds an additional layer of disease classification beyond thetraditional groupings of mild, moderate and severe CF airway disease,based on FEV₁% predicted. Most strategically, transcriptionalclassification of disease severity has utility at the time of APE onset,which would allow for health care providers to devise appropriatetreatment regimens and follow up for the APE episode and beyond,respectively.

As described herein, the present invention comprises a group of tengenes (CD64, ADAMS, CD36, IL32, HPSE, PLXND1, HCA112, CSPG2, TLR2, andCD163) whose expression patterns were found to correlate with varyingdegrees of pulmonary exacerbation disease severity. The inventors havedetermined three clusters or groups in which the expression pattern ofthe genes fall into that correlate with mild severity, moderate severityor severe severity of pulmonary exacerbation and/or disease progression,including increased morbidity and/or mortality associated with severepulmonary exacerbation in subjects having lung diseases such as CF. Itis noted that these genes are not specific to CF and have varying rolesin other conditions characterized by pathologic pulmonary exacerbationand inflammation, including without limitation, asthma, chronicobstructive pulmonary disease, emphysema, interstitial lung disease,bronchitis, acute respiratory distress syndrome and pneumonia (Wu etal., 2008, Am J Respir Crit Care Med 177(7):720-9; Moller et al., 2006,Crit Care Med 34(10):2561-6).

One embodiment of the present invention relates to a method forcategorizing a subject at risk for increased pulmonary exacerbationsand/or disease progression. In one aspect, the subject has experienced apulmonary exacerbation. In one aspect, the method includes obtaining abiological sample from the subject and detecting the expression level ofone or more genes selected from TLR2, ADAM9, PLXND1, CD163, CD36, CD64,CSPG2, IL32, HPSE, HCA112 and combinations thereof. The method alsoincludes comparing the expression level of the one or more genes fromthe subject with gene expression profiles of the same genes known tocorrelate with increased pulmonary exacerbation and/or diseaseprogression. In one aspect, the method further includes categorizing thesubject as at increased risk for increased pulmonary exacerbation and/ordisease progression when the subject's gene expression profilecorrelates to the gene expression profile for increased pulmonaryexacerbation and/or disease progression. In one aspect, the biologicalsample is obtained from the subject at the onset of pulmonaryexacerbation.

Another embodiment of the present invention relates to a method to treata subject at risk for increased pulmonary exacerbation. In one aspect,the subject has experienced a pulmonary exacerbation. In one aspect, themethod includes obtaining a biological sample from the subject anddetecting the expression level of one or more genes selected from TLR2,ADAM9, PLXND1, CD163, CD36, CD64, CSPG2, IL32, HPSE, HCA112 andcombinations thereof. In one aspect, the biological sample is obtainedfrom the subject at the onset of pulmonary exacerbation. The method alsoincludes comparing the expression level of the one or more genes fromthe subject with gene expression profiles of the same genes known tocorrelate with increased pulmonary exacerbation and treating the subjectaggressively if the subject is at risk for increased pulmonaryexacerbation. In one aspect, treating the subject aggressively includesbut is not limited to one or more of the following treatments: alteringthe dosing of the drugs or medication (such as an antibiotic) thesubject is being administered for treating pulmonary exacerbation;altering the quantity of antimicrobial agents the subject is beingadministered; altering the drug(s) selection of the drug(s) the subjecthas been administered for treating pulmonary exacerbation; changing theantimicrobial agent the subject is being administered; administering anantimicrobial agent to the subject; increasing the duration of drug(s)and/or antimicrobial agent(s) the subject is being administered; lungtransplantation; placement on a mechanical ventilator, and/or referralto ICU at a hospital. Subjects determined to be at greater risk forpulmonary exacerbation, such as those determined to be within the mostsevere cluster group as determined by their gene expression profile,will be treated with more aggressive treatment than subjects in the lesssevere cluster groups. For example, a subject determined to be incluster group 3 (severe) as compared to subjects that are determined tobe in cluster groups 1 or 2, would have a more aggressive course oftreatment (such as lung transplant and/or placement on a mechanicalventilator) than those subjects within cluster groups 1 (mild) or 2(moderate) as these subjects would be at a greater risk for pulmonaryexacerbation and/or death.

In one aspect of the embodiments of the invention described herein, thesubject has been diagnosed as having a disease selected from cysticfibrosis, asthma, chronic pulmonary obstructive disease, emphysema,interstitial lung disease, bronchitis, acute respiratory distresssyndrome, and pneumonia. In a preferred embodiment, the subject has beendiagnosed as having cystic fibrosis.

A patient or subject sample can include any bodily fluid or tissue froma patient that may contain the RNA or protein encoded by the genescontemplated here. The term “sample” or “patient sample” or “subjectsample” can be used generally to refer to a sample of any type whichcontains products that are to be evaluated by the present method,including but not limited to, a sample of isolated cells, a tissuesample and/or a bodily fluid sample. According to the present invention,a sample of isolated cells is a specimen of cells, typically insuspension or separated from connective tissue which may have connectedthe cells within a tissue in vivo, which have been collected from anorgan, tissue or fluid by any suitable method which results in thecollection of a suitable number of cells for evaluation by the method ofthe present invention. The cells in the cell sample are not necessarilyof the same type, although purification methods can be used to enrichfor the type of cells that are preferably evaluated. Cells can beobtained, for example, by scraping of a tissue, processing of a tissuesample to release individual cells, or isolation from a bodily fluid.

In some aspects, the sample may comprise blood, sputum, bronchoalveolarlavage or urine. In still some aspects the sample may comprise wholeblood, Peripheral Blood Mononuclear Cells (PBMCs), leuokocytes,monocytes, lymphocytes, basophils, or eosinophils. In a preferredaspect, the sample is whole blood.

A systemic marker of severe pulmonary exacerbation or increasedpulmonary exacerbation disease progression has many advantages, as blood(such as whole blood) can be obtained from subjects of any age anddisease severity, and may reflect the status of exacerbation throughoutthe lung, rather than one segment. This analysis is sensitive,inexpensive, and obtained from blood and/or tissue that is easilyaccessible in pediatric and adult populations, and has the potential tobe performed in a clinical laboratory.

In some aspects, an increased risk of pulmonary exacerbation diseaseprogression includes an increased risk of morbidity and/or mortalityand/or exacerbation recurrence. An increase in pulmonary diseaseprogression leads to a shorter interval of exacerbation free time in thesubject. Morbidity indicates a need for lung transplant or ICUadmission. An increase in morbidity indicates an increase in the subjectbeing referred to lung transplant or having a lung transplant or anincrease in the subject being referred to or being admitted to the ICUor being put on mechanical ventilation. As determined by the inventors,the subject's time to the next exacerbation, and/or time to morbidityand/or five year mortality differed significantly between the clustersor groups, particularly between the most divergent clusters or groups, 1(mild) and 3 (severe). In cluster 1, no subjects needed lung transplantsnor died in the follow-up time-period, while 90% of subjects in cluster3 required ICU transfer for respiratory insufficiency, were referred totransplant, undergone transplant or died over the same time period.

As used herein, the term “expression”, when used in connection withdetecting the expression of a gene, can refer to detecting transcriptionof the gene (i.e., detecting mRNA levels) and/or to detectingtranslation of the gene (detecting the protein produced). To detectexpression of a gene refers to the act of actively determining whether agene is expressed or not and the level at which it expresses. This caninclude determining whether the gene expression is upregulated ordownregulated as compared to a control or as compared to prior toadministration of treatment in the same subject or as compared to geneexpression profiles of the same genes known to correlate with mild,moderate or severe pulmonary exacerbation disease progression and/orpulmonary exacerbation; or unchanged as compared to a control or ascompared to prior to administration of treatment in the same subject oras compared to gene expression profiles of the same genes known tocorrelate with mild, moderate or severe pulmonary exacerbation diseaseprogression and/or pulmonary exacerbation. Therefore, the step ofdetecting expression does not require that expression of the geneactually is upregulated or downregulated, but rather, can also includedetecting that the expression of the gene has not changed (i.e.,detecting no expression of the gene or no change in expression of thegene). When comparing expression levels of genes from the subject withexpression levels of the same genes known to correlate with mild,moderate or severe pulmonary exacerbation disease progression and/orpulmonary exacerbation, the known gene expression levels can be fromexpression levels that have been previously established from populationsof subjects that have been previously identified has having mild,moderate or severe pulmonary exacerbation disease progression and/orpulmonary exacerbation. In one aspect, the expression level of at leastone, at least two, at least three, at least four, at least five, atleast six, at least seven, at least eight, at least nine or all ten ofthe following genes TLR2, ADAM9, PLXND1, CD163, CD36, CD64, CSPG2, IL32,HPSE, HCA112 is compared. When comparing the expression level of one ormore of the genes, to known expression levels, it is to be understoodthat the comparison is to the same genes.

As used herein, reference to a control, means a subject (or populationof subjects) who is a relevant control to the subject being evaluated bythe methods of the present invention. The control can be matched in oneor more characteristics to the subject, including but not limited togender, age and disease severity.

Expression of transcripts and/or proteins is measured by any of avariety of known methods in the art. For RNA expression, methods includebut are not limited to: extraction of cellular mRNA and Northernblotting using labeled probes that hybridize to transcripts encoding allor part of the gene; amplification of mRNA using gene-specific primers,polymerase chain reaction (PCR), quantitative PCR and reversetranscriptase-polymerase chain reaction (RT-PCR), and/or followed byquantitative detection of the product by any of a variety of means;extraction of total RNA from the cells, which is then labeled and usedto probe cDNAs or oligonucleotides encoding the gene on any of a varietyof surfaces; in situ hybridization; flow cytometery and detection of areporter gene. In a preferred aspect, the expression level of the one ormore genes is detected by quantitative PCR.

Methods to measure protein expression levels generally include, but arenot limited to: Western blot, immunoblot, enzyme-linked immunosorbantassay (ELISA), radioimmunoassay (RIA), immunoprecipitation, surfaceplasmon resonance, chemiluminescence, fluorescent polarization,phosphorescence, immunohistochemical analysis, matrix-assisted laserdesorption/ionization time-of-flight (MALDI-TOF) mass spectrometry,microcytometry, microarray, microscopy, fluorescence activated cellsorting (FACS), and flow cytometry, as well as assays based on aproperty of the protein including but not limited to enzymatic activityor interaction with other protein partners. Binding assays are also wellknown in the art. For example, a BIAcore machine can be used todetermine the binding constant of a complex between two proteins. Thedissociation constant for the complex can be determined by monitoringchanges in the refractive index with respect to time as buffer is passedover the chip (O'Shannessy et al., 1993, Anal. Biochem. 212:457;Schuster et al., 1993, Nature 365:343). Other suitable assays formeasuring the binding of one protein to another include, for example,immunoassays such as enzyme linked immunoabsorbent assays (ELISA) andradioimmunoassays (RIA); or determination of binding by monitoring thechange in the spectroscopic or optical properties of the proteinsthrough fluorescence, UV absorption, circular dichroism, or nuclearmagnetic resonance (NMR).

In one aspect of the embodiments of the invention described herein, theexpression level of each of the following ten genes from the subject,TLR2, ADAM9, PLXND1, CD163, CD36, CD64, CSPG2, IL32, HPSE, and HCA112,is detected and compared. In other aspects of the embodiments of theinvention, at least two of the genes, at least three of the genes, atleast four of the genes, at least five of the genes, at least six of thegenes, at least seven of the genes, at least eight of the genes or atleast nine of the genes selected from TLR2, ADAM2, PLXND1, CD163, CD36,CD64, CSPG2, IL32, HPSE, and HCA112 are detected and compared.

In one aspect of any of the embodiments of the invention describedherein, the present invention further comprises measuring FEV₁ and/orCRP levels in the subject.

Another embodiment of the present invention relates to a kit fordetecting the expression of one or more genes selected from CD64, ADAMS,CD36, IL32, HPSE, PLXND1, HCA112, CSPG2, TLR2, and CD163. In one aspect,the kit comprises a detection agent for detecting the expression one ormore of the genes. In one aspect, the kit comprises an agent fordetecting mRNA expression of one or more of the genes. In still anotheraspect, the kit comprises an agent for detecting protein expression ofone or more of the genes.

As described in the examples below, using unsupervised analysis, thegene expression data identifies and categorizes clusters or groups withsignificant differences in disease characteristics and underlyinginflammation. Exacerbation scores, numbers of pathogenic speciesinfecting patients, and time until next exacerbation were allsignificantly different for the “severe” cluster (group 3) compared tothe “mild” cluster (group 1), in both unadjusted and adjusted models forage, cell counts, and FEV₁. The ability of gene variables todiscriminate clinically distinct patient groups provides validity totheir significance. Leukocyte gene expression can discern inflammatorydifferences between groups in a manner which is not achievable usingstandard measures of lung function or inflammation, since FEV₁%predicted, white blood cell count and CRP did not differ betweenclusters. Of great interest, the clusters identify and categorizepatients with variable patterns of systemic inflammation, based onleukocyte differentials. Subjects with longer disease free intervalsmanifest more lymphocytic predominance in the peripheral blood at theonset of exacerbation, while those who go on to exacerbate at shorterintervals are predominantly more neutrophilic. To address the impact ofthese differences on cluster membership, adjustments were made for cellcounts. Nonetheless, the transcripts maintained their significance toidentify differences between clusters, indicating that underlyingdifferences in transcription, and not solely cell numbers, exist betweengroups. The inventors examined the relationship between clusters and 5year morbidity and survival. While FEV₁ had a significant associationwith mortality, gene expression also defined differences in survival, asseen between clusters 2 and 3. Furthermore, the validation analysisdemonstrated that cluster membership based on gene expression couldaccurately predict morbidity and mortality in a separate cohort, despitedifferent mean FEV₁ values in the validation cohort.

Gene differences allow clinicians to distinguish pathobiologicdifferences between patients with APE. The top gene responsible fordifferences in cluster membership was TLR2, whose strong signal in thesickest subjects is clearly highlighted in the heat map presented inFIG. 6. The most highly represented genes in the sickest cluster closelylink to pathways of microbial recognition. Thus, the TLR2 receptor bindsboth gram-negative and gram-positive bacterial muropeptides, as aprimary line of host defense (Johnson C M, Tapping R I. Microbialproducts stimulate human Toll-like receptor 2 expression through histonemodification surrounding a proximal NF-kappaB-binding site. J Biol Chem2007; 282:31197-205; Muller-Anstett M A, Muller P, Albrecht T, et al.Staphylococcal peptidoglycan co-localizes with Nod2 and TLR2 andactivates innate immune response via both receptors in primary murinekeratinocytes. PLoS One 2010; 5:e13153). CD163, a monocyte/macrophagescavenger receptor, dually binds both gram negative and gram positivebacteria (in comparison to TLR2 binding of microbial products) to inducepro-inflammatory cytokine responses (Fabriek B O, van Bruggen R, Deng DM, et al. The macrophage scavenger receptor CD163 functions as an innateimmune sensor for bacteria. Blood 2009; 113:887-92). CD64 enactsneutrophil and macrophage phagocytic host defense, once microbialsurface ligands contact the CD64 receptor (Huang Z Y, Hunter S, Chien P,et al. Interaction of two phagocytic host defense systems: Fcgammareceptors and complement receptor 3. J Biol Chem 2011; 286:160-8). Andfinally, ADAM9, expressed by both neutrophils and monocytes, promotesleukocyte extravasation by degrading extracellular membrane protein,elastin, to assist with ongoing leukocyte recruitment (Roychaudhuri R,Hergrueter A H, Polverino F, et al. ADAM9 Is a Novel Product ofPolymorphonuclear Neutrophils: Regulation of Expression andContributions to Extracellular Matrix Protein Degradation during AcuteLung Injury. J Immunol 2014; 193:2469-82). The link between diseaseprogression and innate immune activation is both transcriptional andtranslational, since enhanced downstream production of pro-inflammatoryproteins was present in the “severe” cluster (group 3), while lacking inIFNγ, which is essential for bacterial clearance (Moser C, Kjaergaard S,Pressler T, Kharazmi A, Koch C, Hoiby N. The immune response to chronicPseudomonas aeruginosa lung infection in cystic fibrosis patients ispredominantly of the Th2 type. APMIS 2000; 108:329-35; Brazova J, SedivaA, Pospisilova D, et al. Differential cytokine profile in children withcystic fibrosis. Clin Immunol 2005; 115:210-5). While it is notsurprising that seriously ill patients with CF have evidence ofexcessive innate activation, it is novel that a diversity of responsesexists between subjects without significant differences in lungfunction, that this heterogeneity is quantifiable, and that biologicaldifferences in innate activation facilitate prognostication.

Prognostication based on host immune responses has been studiedextensively in CF (Wojewodka G, De Sanctis J B, Bernier J, et al.Candidate markers associated with the probability of future pulmonaryexacerbations in cystic fibrosis patients. PLoS One 2014; 9:e88567; LiouT G, Adler F R, Keogh R H, et al. Sputum biomarkers and the predictionof clinical outcomes in patients with cystic fibrosis. PLoS One 2012;7:e42748; Downey D G, Martin S L, Dempster M, et al. The relationship ofclinical and inflammatory markers to outcome in stable patients withcystic fibrosis. Pediatr Pulmonol 2007; 42:216-20). Biomarkers from theblood and sputum have not reproducibly predicted short and longer termoutcomes. The longstanding gold standard predictor of survival has beenFEV₁ (Rosenthal M. Annual assessment spirometry, plethysmography, andgas transfer in cystic fibrosis: do they predict death ortransplantation. Pediatr Pulmonol 2008; 43:945-52; Kerem E, Reisman J,Corey M, Canny G J, Levison H. Prediction of mortality in patients withcystic fibrosis. N Engl J Med 1992; 326:1187-91). Not uncommonly, anypotential association of biomarkers with CF outcomes, such as survival,may be lost once adjustment occurs for lung function, given its powerfulinfluence on mortality (Moffitt K L, Martin S L, Jones A M, et al.Inflammatory and immunological biomarkers are not related to survival inadults with Cystic Fibrosis. J Cyst Fibros 2014; 13:63-8). However, inclinical practice, FEV₁ lacks sensitivity. For example, the “severe”cluster (group 3), with a 90% morbidity and mortality rate at 5 years,had an average FEV₁ of 37% predicted. While an FEV₁ of <30% had a 2-yearmortality of 50% in 1992²⁸, its 2 year mortality was estimated to beless than 20% as of 2011 (George P M, Banya W, Pareek N, et al. Improvedsurvival at low lung function in cystic fibrosis: cohort study from 1990to 2007. BMJ 2011; 342:d1008). Thus FEV₁ alone would not have predictedthe severe cluster's outcomes. Furthermore, FEV₁ offers no diagnosticinsight into particular inflammatory pathways which may predominate in aparticular patient. There are no established guidelines for mild,moderate or severe disease based on FEV₁ except to refer a subject tolung transplant once the subject's FEV₁ falls below 30% predicted. Therehave been no blood biomarkers which can reproducibly partition subsetsof patients at increased risk for exacerbation recurrence over time.Single studies exist which demonstrate blood biomarkers, such as serumcalprotectin, whose levels may predict time until next exacerbation(Gray R D, Imrie M, Boyd A C, Porteous D, Innes J A, Greening A P.Sputum and serum calprotectin are useful biomarkers during CFexacerbation. J Cyst Fibros 2010; 9:193-8). CRP has shown variableeffect in its ability to predict time until next exacerbation (Gray R D,Imrie M, Boyd A C, Porteous D, Innes J A, Greening A P. Sputum and serumcalprotectin are useful biomarkers during CF exacerbation. J Cyst Fibros2010; 9:193-8; Sequeiros T M, Jarad N. Factors associated with a shortertime until the next pulmonary exacerbation in adult patients with cysticfibrosis. Chron Respir Dis 2012; 9:9-16). As demonstrated by theinventors herein there were no significant differences in FEV₁ or in CRPbetween the 3 clusters or groupings, despite differences in morbidityand mortality between groups, highlighting the shortcomings of thesecurrent standard measures of disease.

In the examples below, gene expression of the gene panel disclosedherein was measured cross-sectionally and not longitudinally. Thus,expression data was not available for multiple exacerbations within thesame individual. It is also contemplated herein that expression is bemeasured at the onset of multiple exacerbations, in concert with FEV₁,in order to pinpoint systemic trends in inflammation as diseaseprogresses and the point at which maladaptive immune responses begin topredominate. While the assessment of gene expression at the onset of asingle exacerbation gives insight into long term mortality, multiplemeasurements may also be taken to provide further accuracy in riskprediction. In addition, it is contemplated herein to include bacterialexpression analysis to assess virulence factors and their impact onexpression along with the cluster analysis disclosed herein. The role ofbacterial pathogenicity is critical in better understanding howbacterial antigens influence signaling down innate immune pathways in amanner to induce excessively harmful inflammation in the host.Nevertheless, the fact that the gene expression signature continued tohave prognostic value for host response and outcomes, despite a varietyof bacterial infections in study subjects, is important in terms of itsreal world applicability.

Expression of the gene panel disclosed herein is believed to allow formolecular phenotyping in CF, allowing stratification of treatmentaccording to the underlying biology for a particular exacerbation. Thestability of cluster delineation was supported by a second independentcohort, with an accuracy of 80-90% in predicting morbidity. Itdemonstrates that gene expression allows risk prediction forlongitudinal health outcomes from peripheral blood draws, can allow moreprecision both to the development of individually tailored regimens andto strategies for timing of transplant referral.

The following examples are provided for illustrative purposes, and arenot intended to limit the scope of the invention as claimed herein. Anyvariations which occur to the skilled artisan are intended to fallwithin the scope of the present invention.

EXAMPLES

The examples presented below demonstrate that whole blood mRNAtranscripts allow molecular categorization of disease endotypes in CF,providing a tool both for distinguishing and treating subjects who areat increased risk for pulmonary exacerbation and disease progression andmortality, and for eliciting distinct pathobiological mechanisms whichunderlie diversity in host outcomes.

Methods:

Transcript abundance for a leukocyte 10-gene panel was measured fromwhole blood, in a cohort of adult CF subjects (n=57) at the beginningand end of treatment for an acute pulmonary exacerbation. A hierarchicalcluster analysis was performed on the gene expression data at thebeginning of treatment. Clusters were analyzed for differences in FEV₁(% predicted), CRP (log transformed), return to baseline FEV₁ at the endof treatment, time to next exacerbation and time to death, transplant,transplant referral or mechanical ventilation. A discriminant analysiswas performed in a separate cohort as a validation of the ability ofcluster membership to predict future outcomes in a separate population.

Full study design details of the CF circulating mRNA study havepreviously been reported (Nick J A, Sanders L A, Ickes B, et al. BloodmRNA biomarkers for detection of treatment response in acute pulmonaryexacerbations of cystic fibrosis. Thorax 2013; 68:929-37). The study wasa 3-year (2008-2011), single center, prospective observational study of57 CF subjects, over the age of 18, suffering from APE. Diagnosis ofexacerbation was based on the Cystic Fibrosis Foundation definedclinical practice guidelines (Foundation C F. Microbiology andinfectious diseases in cystic fibrosis: V (Section 1). Bethesda, Md.;1994), and all subjects underwent calculation of an exacerbation scoreto confirm presence of exacerbation (Rosenfeld M, Emerson J,Williams-Warren J, et al. Defining a pulmonary exacerbation in cysticfibrosis. J Pediatr 2001; 139:359-6). Subjects were deemed eligibleregardless of pathogen, disease severity or mutation. Study enrollmenthad no impact on treatment interventions. All enrolled subjectscontinued to receive APE therapy as directed by their physician. Datawere collected at the initiation and conclusion of treatment of APE.

In all enrolled subjects, transcript abundance was quantified byquantitative PCR (qPCR) in pre and post antibiotic samples, for thefollowing ten genes: CD36, CD64, CD163, toll-like receptor 2 (TLR2),plexin D1 (PLXND1), hepatocellular carcinoma associated antigen 112(HCA112), heparanase (HPSE), a disintegrin and metalloproteinase domain9 (ADAM9), versican (CSPG2), and IL-32 (Nick J A, Sanders L A, Ickes B,et al. Blood mRNA biomarkers for detection of treatment response inacute pulmonary exacerbations of cystic fibrosis. Thorax 2013;68:929-37). All subjects who had transcripts analyzed underwentsimultaneous phlebotomy for serum. Serum samples were frozen at −80° C.and later underwent ELISA analysis for IL-1b, IL-6 and IFNg proteins(Mesoscale, Rockville, Md.).

Cluster analysis of transcript abundance was performed via ahierarchical clustering algorithm based on Euclidean distances, usingthe Ward minimum variance method, such that within-cluster variation wasminimized (Brazova J, Sediva A, Pospisilova D, et al. Differentialcytokine profile in children with cystic fibrosis. Clin Immunol 2005;115:210-5). Dendrograms were produced and visually determined separationbetween clusters. To compare differences among clusters, ANOVA andFisher's exact tests were used for continuous and categorical variables,respectively. Linear regression models were used to allow for theaddition of cell count covariates, including neutrophil, lymphocyte, andmonocyte counts across clusters.

Kaplan Meier analyses were used for time to event outcomes of interest.Log rank tests were employed to determine differences between clustersin the Kaplan Meier analyses. Age and FEV₁ adjusted time to eventanalyses were modeled with Cox proportional hazards regressions. Whenoverall tests were significant, pairwise differences were examined forsignificance. Neutrophil, lymphocyte and monocyte cell counts wereconsidered in models, and backwards eliminated performed based on asignificant p-value cutoff of 0.15. Cell count percentages were alsoconsidered but did not significantly differ from raw cell counts.

A linear discriminant analysis based on equal variances between clusterswas performed on an independent cohort of ten subjects who were notincluded in the training set. Prior probabilities were set to beproportional to cluster size. Posterior probabilities of membership intoclusters designated in the training set were calculated for the testset, for time until next exacerbation and time until morbidity/mortalityevent (transplant, transplant referral, ICU admission or death). Inaddition, a linear discriminant analysis was also performed on theoriginal 57 subjects, where 70% of the subjects were randomly selectedfor the training set, and the remaining 30% were selected for thetesting set. All tests were 2-tailed with an alpha level of 0.05. Allanalyses were conducted with SAS version 9.3 (SAS Institute Inc, Cary,N.C.).

Overall Results of the Examples Presented Below:

CF host transcript abundance reflects variability in immune responses tostandard treatment algorithms for CF exacerbations. At the initiation ofexacerbation therapy, 3 distinct subject clusters were identified. FEV₁,CRP (log transformed) and return to baseline FEV₁ following treatmentwere not different between clusters. However, time to next exacerbationp<0.0001), and time to morbidity (lung transplant referral or ICUadmission) and 5 year mortality differed significantly between clusters(p=0.0002), particularly between the most divergent clusters, 1 (mild)and 3 (severe). In cluster 1, no subjects have been transplanted norhave died in follow-up, while 90% of subjects in cluster 3 required ICUtransfer for respiratory insufficiency, have been referred totransplant, have undergone transplant or have died over this time period(p=0.0001). Six genes as noted in Example 1 below were significant indetermining cluster membership between groups 1 and 3 (p<0.05 for allgenes).

Example 1 Cluster Analysis Resulted in Categorization of ExacerbationSamples into Three Subgroups of Subjects Based on Gene Expression

Three distinct clusters were partitioned and categorized from the data,based on a fixed semi-partial R squared distance of approximately 0.075between clusters (FIG. 1). Cluster groups were comprised of sample sizesof n=10 (Cluster 1/mild), n=37 (Cluster 2 /moderate), and n=10 (Cluster3/severe). Subjects in cluster 1 were distinguished by significantlylower expression of ADAM9, CD163, and TLR2 compared to those in bothclusters 2 and 3 (FIG. 2). The greatest magnitudes of expressiondifferences were seen between Clusters 1 and 3. Cluster 1 expression ofADAM9, CD64, CD163, IL32, HCA112 and TLR2 was significantly differentfrom those subjects in cluster 3. Rank of importance was calculated forthe genes which most highly determined cluster membership or category,with the following rank in descending order (with accompanying Fstatistic from the ANOVA test): 1. TLR2 (45.77), 2. ADAM9 (31.85), 3.PLXND1 (21.31), 4. CD163 (19.55), 5. CD36 (16.09), 6. CD64 (14.98), 7.CSPG2, 8. IL32(11.7), 9. HPSE (9.61), and 10. HCA112 (6.17).

The test cohort included 57 adult subjects who underwent hierarchicalclustering based on gene expression at the onset of pulmonaryexacerbations. Demographic data and sample sizes of the three identifiedclusters as well as cluster-specific clinical outcomes are shown inTable 1. Clusters are labeled 1, 2, and 3, or, mild, moderate and severerespectively (based on clinical outcomes). There were no significantdifferences between age, diabetic status, FEV₁% predicted, or DF508homozygote status between the three clusters. In addition, the return tothe previous year's baseline FEV₁ following exacerbation treatment didnot differ significantly between clusters. Cluster 3 had significantlyhigher exacerbation scores, more co-infections identified fromrespiratory cultures, and a non-significant trend towards lower lungfunction. Characteristics of the validation cohort are described inExample 5.

TABLE 1 Characteristics of study clusters Cluster 1 Cluster 2 Cluster 3p value No. of subjects 10  37 10  Age (mean +/− SD) years 30 ± 11 32 ±10 28 ± 7  0.42 Gender- no. (% female) 7 (70) 25 (68) 4 (40) 0.29Genotype no. (%) ΔF508/ΔF508 (%) 5 (50) 21 (57) 4 (40) 0.64^($) Other(%) 5 (50) 16 (43) 6 (60) FEV₁ (mean +/− SD % predicted)* 53 ± 17 42 ±17 37 ± 15 0.1 Return to ≥90% peak FEV1 from 8 (89) 28 (85) 7 (78) 0.86previous year, post-therapy (%)* Exacerbation severity (mean +/− 4.52 ±0.88 5.02 ± 1.65 6.46 ± 1.07 0.009 SD Rosenfeld score)~ Sputum culture~Pseudomonas aeruginosa only (%) 5 (50) 15 (41) 0 0.02^($) Pseudomonasaeruginosa + 5 (50) 22 (59) 10 (100) Staphylococcus aureus, or otherpathogens (%) Health care utilization in follow-up Admissions insubsequent year no. (%)~ None 7 (70) 15 (42) 0 0.003^($) 1+ 3 (30) 21(58) 10 (100) Admissions to ICU/mechanical 0  3 (8.1) 2 (20) 0.39ventilation (%) Awaiting lung transplant or 0   8 (21.6) 8 (80) <.0001s/p referral (%)~ Lung transplantation (%) 0   5 (13.5) 1 (10) 0.82Death (%) 0  8 (22) 4 (40) 0.11 Composite: Death, lung transplantion, 012 (32) 9 (90) <.0001 or referral to lung transplant** ^($)statisticrepresents comparison of the proportion between the indicated row andthe row immediately underneath *6 subjects missing prior FEV₁ data (1from cluster 1, 4 from cluster 2, and 1 from cluster 3) ~Cluster 1 and 2significantly different from Cluster 3. **Statistic calculated as acomposite score of 3 variables. Signficant differences found across allthree clusters.

Example 2 Cluster Assignment and Cross-Sectional Analysis of SystemicInflammation and Pulmonary Function

Column graphs in FIG. 3 reflect variations in standard measures ofinflammation between the cluster subgroups. WBC and CRP values (both logtransformed) were not significantly different between clusters(Bartlett's test for homogeneity of variance across clusters, p=0.73 and0.89 respectively). Despite a decreasing trend from cluster 1 to cluster3, FEV₁% predicted did not vary significantly between clusters(Bartlett's test for homogeneity of variance, p=0.86). When white bloodcell differential counts were compared, neutrophil, lymphocyte andmonocyte counts between clusters did differ. Peripheral lymphocytecounts were higher in cluster 1 versus 3 (p=0.0008) and cluster 1 versus2 (p=0.0008). Peripheral neutrophil percentages were lower in cluster 1versus 3 (p=0.0008) and cluster 2 versus 3 (p=0.02). No significantdifferences were found across clusters for peripheral monocyte counts.

Example 3 Cluster Assignment and Short Term Outcomes after Exacerbation

To determine whether exacerbation outcomes differed between the threeclusters, the subjects' return to baseline FEV₁ following treatment wasevaluated, which was defined as achievement of >90% of best FEV₁ in theyear prior to study with treatment. There were no differences betweenclusters in reaching baseline FEV₁ following treatment of exacerbation(p=0.86, Fisher's exact test). Persistent systemic inflammation attreatment end, based on abnormally elevated CRP (>0.4 mg/dL), wascompared across clusters. Incidence of elevated post-treatment CRP wasnot significantly different between clusters (p=0.84, Fisher's exacttest). However, significant differences in time to subsequent pulmonaryexacerbation occurred between clusters. A survival analysis (FIG. 4)revealed that individuals in Clusters 1 and 2 had longer intervals ofexacerbation free time, compared to those individuals in cluster 3(median times for clusters 1, 2 and 3 were 314.5, 218 and 85 days,respectively), and these comparisons were statistically significant(p=0.0006 for Cluster 1 versus 3; p<0.0001 for Cluster 2 versus 3;overall p<0.0001). After adjusting for baseline FEV₁, age, andneutrophil cell counts (the last of which remained in the model afterbackward selection), the differences between clusters in time toexacerbation event remained significant (Cluster 3 versus 1, hazardratio or HR=3.9, p=0.03; and Cluster 3 versus 2, HR 3.8, p=0.002). Inmodels with the cluster variable alone, Cluster 3 versus 1 had a HR of6.6, p=0.0002, and Cluster 3 versus 2 had a HR of 4.7, p=0.0001. Theability of FEV₁ alone to predict time to next exacerbation hadborderline significance (p=0.05).

Example 4 Cluster Assignment and Future Indicators of DiseaseProgression, Morbidity and Mortality

To determine whether cluster membership in a certain category couldpredict other major events characteristic of disease progression, timeto the following was evaluated for subjects within each cluster:mechanical ventilation or ICU admission for pulmonary status (notrelated to transplant), referral to lung transplantation for end-stagedisease (FEV1<30% predicted or loss of treatment response toantibiotics), actual lung transplantation, or death. A survival analysisby the Kaplan-Meier method revealed longer intervals of morbidity andmortality free time for subjects in clusters 1 and 2 compared to cluster3 (cluster 1 versus 3, p<0.0001; cluster 2 versus 3, p=0.006; overallp=0.0002; median survival times not estimable for clusters 1 and 2 dueto no or few events in these clusters) (FIG. 5). CF subjects whose geneexpression fell within Cluster 1 had a 0% rate of significant events(increase in morbidity, or mortality), while CF subjects whose geneexpression fell within Cluster 3 had a 90% rate of major events(p=0.0001, Fisher's exact test). Cox proportional hazards regression wasused to adjust for the effect of differences in baseline FEV₁, age andmonocyte cell counts (which remained in the model after backwardsselection) on survival. Unadjusted p values rose following adjustment,primarily due to FEV₁ effect on survival. Yet, significant differencesremained between clusters 2 and 3 (HR=5, p=0.004; compared with HR=3.2,p=0.009 for the model with cluster variable alone), in long termsurvival and morbidity based on gene expression. Hazard ratios forcluster 1 could not be computed since no events occurred in that group.FEV₁ alone was a predictor of major events (p=0.005).

Example 5 Validation of Cluster Categories

As a validation, a linear discriminant analysis was performed for thegene predictors and their ability to predict increased morbidity andmortality, as defined above, in a separate population (n=10). Thecomparison demographics for both training and test cohorts are shown inTable 2. The validation cohort overall was healthier, based on higherpre- and post-APE treatment FEV₁% predicted. The group also had notsuffered any morbidity or mortality events as defined above at 2 yearsfollow-up. Two separate validation approaches were taken. First, adiscriminant analysis of 57 subjects from the original dataset wasutilized to predict morbidity and mortality in the validation dataset of10 additional subjects. Even though the mean FEV₁ was higher forvalidation subjects than for the main study subjects (p<0.05), theprobability of genes to predict significant morbidity or mortalityevents was 90%. A second validation approach was performed where atraining and test dataset were created from the 57 original subjects,randomly splitting them into 2 groups. A 3:1 ratio was used in theallocation, yielding 40 subjects to fit the model and 17 for validation.In this analysis, genes discerned morbidity and mortality with an 82%probability.

TABLE 2 CHARACTERISTICS OF STUDY COHORTS Training cohort Test cohortP-value No. of subjects 57 10  Age in years, mean ± SD{circumflex over( )} 31 ± 10 34 ± 17 0.52 Gender - no. female (%) 36 (63) 6 (60) 1.00Genotype - no. ΔF508/ΔF508 (%) 30 (53) 4 (40) 0.51 FEV₁ % of predicted,mean ± SD Pre 43 ± 17 62 ± 22 0.02 Post 53 ± 18 74 ± 20 0.01 Difference(Post − Pre) 10 ± 9  11 ± 7  0.49 Return to > 90% peak FEV₁ fromprevious year* 43 (84) 8 (80) 0.66 Exacerbation severity (mean Rosenfeldscore){circumflex over ( )} 5.2 ± 1.6 5.9 ± 1.3 0.13 Sputum culture -no. (%) 0.48 Pseudomonas aeruginosa only 20 (35) 5 (50) P. aeruginosa +S. aureus, or other pathogens (%) 37 (65) 5 (50) Health care utilizationin follow-up Admissions in subsequent year no. (%) 1.00 None 22 (39) 4(40) 1+ 34 (60) 6 (60) Admissions to ICU/mechanical ventilation  5 (8.9)0 1.00 Referral to lung transplant 16 (29) 0 0.10 Lung transplantation 6 (11) 0 0.58 Death 13 (24) 0 0.19 {circumflex over ( )}P-value from 2sample t-test assuming unequal variances. Otherwise, p-value fromFisher's exact test. *From n = 51 subjects who had previous year's FEV₁available

Example 6 Distinct Biological Pathways Specific to Exacerbations andAssociation of Gene Expression with Downstream Pro-Inflammatory Proteins

A heat map was generated to depict gene differences between clusters(FIG. 6). Since the TLR2 gene most highly influenced cluster membership,from the 10 gene panel, subjects within clusters were tested forevidence of downstream differences in protein expression related toinflammation.

In addition, the association between transcript abundance, serum IL-6(representing the acute-phase response), and IFNγ (representingbacterial clearance) cytokine concentrations was measured in clustermembers who had serum available for testing. Serum collected at the sametime of mRNA isolation was analyzed from Cluster 1 (n=6 of 10 totalsamples), Cluster 2 (n=9 of 37 samples), and Cluster 3 (n=7 of 10samples). Gene expression of CD36, a co-receptor for the TLR2heterodimer, was positively associated with serum IL-6 concentrations(r=0.45, p=0.03) (FIG. 7A). However, TLR2 and CD163 expression werenegatively correlated with serum IFNγ concentrations (r=−0.42, p=0.047;r=−0.45, p=0.03) (FIG. 7B).

While various embodiments of the present invention have been describedin detail, it is apparent that modifications and adaptations of thoseembodiments will occur to those skilled in the art. It is to beexpressly understood, however, that such modifications and adaptationsare within the scope of the present invention, as set forth in thefollowing exemplary claims. Each publication and reference cited hereinis incorporated herein by reference in its entirety.

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What is claimed:
 1. A method of determining pulmonary diseaseprogression severity in a subject having cystic fibrosis and treatingthe subject, wherein the subject has experienced a pulmonaryexacerbation, the method comprising a. obtaining a whole blood samplefrom the subject at the time of pulmonary exacerbation; b. detecting themRNA expression level of each of the following genes: TLR2, ADAM9,PLXND1, CD163, CD36, CD64, CSPG2, IL32, HPSE, HCA112; c. determining theseverity of the pulmonary disease progress in the subject by calculatinga disease risk score of mild, moderate or severe pulmonary diseaseprogression based on the subject's combined mRNA expression level of thegenes from step (b) at the time of pulmonary exacerbation, wherein thesubject's calculated disease risk score correlates to the risk score formild, moderate or severe pulmonary disease progression at exacerbation;and d. treating the subject with treatments effective for treating mild,moderate or severe pulmonary disease progression.
 2. The method of claim1, wherein the mRNA expression level of the genes is detected byquantitative PCR or flow cytometery.
 3. The method of claim 1, wherein asubject having severe pulmonary exacerbation disease progression willhave an increased risk of morbidity.
 4. The method of claim 1, wherein asubject having severe pulmonary exacerbation disease progression willhave an increased risk of mortality.
 5. The method of claim 1, wherein asubject having severe or moderate pulmonary exacerbation diseaseprogression will have an increased risk for exacerbation recurrence. 6.The method of claim 1, wherein a subject having severe or moderatepulmonary exacerbation disease progression will have a shorter intervalof exacerbation free time.
 7. The method of claim 1, further comprisingmeasuring the subject's forced expiratory volume (FEV₁) and/orC-reactive protein (CRP) levels.